沈阳航空航天大学学报2023，Vol.40Issue(5)：32-37,6.DOI:10.3969/j.issn.2095-1248.2023.05.005
基于BRWSSA-GRU的飞机发动机滑油系统故障诊断
Fault diagnosis of aircraft engine lubricating oil system based on BRWSSA-GRU
摘要
Abstract
In response to the problem of unstable fault diagnosis performance of neural network caused by artificially selected parameters,as well as the problems of narrowing the optimization range and fall-ing into the local optima caused by the randomness of sparrow search algorithm(SSA)population ini-tialization,opposition-based learning(OBL)was used to optimize the initialization process of sparrow population in SSA algorithm and expand the search range.Combined with the random walk strategy(random walk,RW),the optimal sparrow in the optimization process was disturbed to improve the lo-cal search ability of the algorithm and reduce the risk of the algorithm falling into local optimum.On this basis,an improved BRWSSA algorithm was used to optimize the number of hidden layer nodes of gate recurrent unit(GRU),and a fault diagnosis model of engine oil system based on BRWSSA-GRU was designed.In order to verify the effectiveness of the fault diagnosis model,two fault diagnosis mod-els,GRU and SSA-GRU,were also designed.Finally,comparative experiments were conducted to vali-date three different fault diagnosis models,GRU,SSA-GRU,and BRWSSA-GRU using the same lubri-cating oil system dataset.The results show that the diagnostic accuracy of the proposed BRWSSA-GRU fault diagnosis model is obviously better than that of GRU and SSA-GRU methods,which veri-fies the effectiveness of the designed BRWSSA-GRU fault diagnosis model.关键词
滑油系统/麻雀搜索算法/随机游走策略/反向学习/门控循环单元Key words
lubricating oil system/sparrow search algorithm/random-walk strategy/opposition-based learning/gate recurrent unit分类
信息技术与安全科学引用本文复制引用
崔建国,徐伟,崔霄,于明月,王宇琦,唐晓初..基于BRWSSA-GRU的飞机发动机滑油系统故障诊断[J].沈阳航空航天大学学报,2023,40(5):32-37,6.基金项目
国家自然科学基金(项目编号:51605309) （项目编号:51605309）
中国航空科学基金(项目编号201933054002) （项目编号201933054002）
